Representational learning on graphs
Project description
jwalk performs random walks on a graph and learns representations for nodes using Word2Vec. It also has options to train existing models online and specify weights.
Install
pip install -U jwalk
Build
make build
Usage
jwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' '
To see the full list of options:
jwalk --help Prompt parameters: debug: drop a debugger if an exception is raised delimiter: delimiter for input file embedding-size: dimension of word2vec embedding (default=200) has-header: boolean if csv has header row help (-h): argparse help input (-i): file input (edgelist of 2/3 cols or adjacency matrix) log-level (-l) logging level (default=INFO) model (-m): use a pre-existing model num-walks (-n): number of of random walks per graph (default=1) output (-o): file output stats: boolean to calculate walk statistics [requires pandas] undirected: make graph undirected walk-length: length of random walks (default=10) window-size: word2vec window size (default=5) workers: number of workers (default=multiprocessing.cpu_count)
Input File
The input file can be of the following formats:
Edgelist: CSV with 2 or 3 columns denoting the source, target and (optional) weight. There are CLI options to specify the delimiter and whether the file has a header (default=False). The CSV file is loaded using numpy if pandas is not installed. We strongly recommend using pandas to load the CSV as it’s a lot faster.
Graph: If the file has an extension that is “.npz”, jwalk will assume that it is a SciPy CSR matrix. Included must be keys of data, indices, indptr, shape and labels (default=None) where labels are the node labels. For an example, see tests/data/karate.npz.
Test
Running unit tests:
make test
Running linter:
make lint
Running tox:
make test-all
Blog
Read more about jwalk in our blog post here: https://www.jwplayer.com/blog/deepwalk-recommendations/
License
Apache License 2.0
References
[paper]: arXiv:1403.6652 [cs.SI] “DeepWalk: Online Learning of Social Representations”
[paper]: arXiv:1607.00653 [cs.SI] “node2vec: Scalable Feature Learning for Networks”
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